The proposed study aims to advance the study of pain research by exploring feasibility of the crowdsourcing approach for eliciting and analyzing the way in which individuals experience and understand chronic pain. We will leverage an existing NIH/NCCIH grant (RAND Center of Excellence in Research on CAM; CERC) to conduct a feasibility study of new methods for gathering and analyzing data on chronic pain through three specific aims: Aim 1: Explore comparability and reliability of crowdsourced data to patient interview data. We will explore the similarity between data from the online crowdsourced participants who self-report chronic pain with data collected via telephone for the CERC study on patients receiving clinical care for chronic pain. We will analyze individual's demographics, experiences with chronic pain and outcomes measures on pain and function. Specifically, we will compare data on a) the length of pain they experienced; b) types of providers and modalities utilized; c) if they consider this chronic pain; d) how they define chronicity; how they describe te shift from acute to chronic; e) questions from the NIH Low Back Pain Task Force that categorize patients as chronic; and f) quantitative measures on pain and function. We will also draw subsamples of crowdsourced data to assess reliability. Aim 2: Develop and test a crowdsourcing approach for analyzing chronic pain definitions. We will ask the online participants to act as analysts and code qualitative texts that contain data on definitions of chronic pain that, were collected within Aim 1. The themes coded will be compared using inter-rater reliability to those generated by expert coders working within the existing NIH/CERC study. Aim 3: Assess efficiency and quality of crowdsourced data as compared to CERC data. We draw quantitative comparisons of cost (labor/incentives), time, data quality (amount of text, missing data) across online crowdsourced and CERC study samples. The proposed work will advance the science of eliciting individual pain experiences by determining feasibility replicability, and efficiency of an innovative online approach which promises alternatives to expensive and time consuming in-person methods.